DART is introduced, a training-free routing framework that samples two cheap no-think drafts, accepts direct answering when the drafts agree, and predicts a thinking budget from draft entropy when they disagree, and preserves or improves always-thinking accuracy in most settings while reducing thinking-token use.
Jungseob Lee, Seongtae Hong, Seungjun Lee et al.· arXiv.org· 1 citation
Because the signal spans a contiguous layer band, LayerMix aggregates it to match oracle-layer performance without oracle access, and characterize the geometry within the controlled paired-example paradigm.
Jungseob Lee, Jaehyung Seo, Heu-Jeoung Lim· 1 citation
This paper proposes Cross-lingual Ranking Preference Optimization~ (CRPO), a novel framework that leverages robust preference knowledge from English to facilitate preference alignment in the target language, thereby enhancing language adaptation and output quality.
Seungyoon Lee, Minhyuk Kim, Jungseob Lee et al.· 0 citations
This work releases LAMAR, a language aware multilingual cross encoder trained to account for both semantic relevance and language coherence, which achieves the best performance overall and across all languages examined individually on general multilingual reranking benchmarks.
Seongtae Hong, Youngjoon Jang, Jungseob Lee et al.· arXiv.org· 0 citations
This work shows that training a strong instruction-tuned reasoning model on its own answer-conditioned chains sharply lowers its verifiable-reasoning accuracy, and generates answer-blind data, because no correctness filter can see this damage in the data.
Jungseob Lee, Seungyoon Lee, Suhyune Son et al.· arXiv.org· 0 citations
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